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Ten principles of complexity and enabling infrastructures

Authors:
COMPLEX SYSTEMS AND EVOLUTIONARY PERSPECTIVES ON
ORGANISATIONS:
THE APPLICATION OF COMPLEXITY THEORY TO ORGANISATIONS
ELSEVIER 2003, ISBN: 0-08-043957-8
Ten Principles of Complexity & Enabling Infrastructures
Professor Eve Mitleton-Kelly
Director
Complexity Research Programme
London School of Economics
Visiting Professor
Open University
All material, including all figures, is protected by copyright and should not be reproduced
without the express permission of the author.
Chapter 2
Introduction
If organisations are seen as complex evolving systems, co-evolving within a social
‘ecosystem’, then our thinking about strategy and management changes. With the
changed perspective comes a different way of acting and relating which could lead to a
different way of working. In turn, the new types of relationship and approaches to work
could well provide the conditions for the emergence of new organisational forms.
This chapter will offer an introduction to complexity by exploring ten generic
principles of complex evolving systems (CES) and will show how they relate to social
systems and organisations. These are not the only principles of CES, but gaining an
understanding of these ten principles and how they relate to each other, could provide a
useful starting point for working with them and applying them to the management of
firms. An example of how a department of an international bank, in one geographic
location, changed its way of working from the different dominant culture, will be given
at the end to illustrate the proposition that providing the appropriate socio-cultural and
technical conditions could facilitate the emergence of new ways of working and relating.
There is no single unified Theory of Complexity, but several theories arising from
various natural sciences studying complex systems, such as biology, chemistry,
computer simulation, evolution, mathematics, and physics. This includes the work
undertaken over the past four decades by scientists associated with the Santa Fe Institute
(SFI) in New Mexico, USA, and particularly that of Stuart Kauffman (Kauffman 1993,
1995, 2000) John Holland (Holland 1995, 1998), Chris Langton (Waldrop 1992), and
Murray Gell-Mann (1994) on complex adaptive systems (CAS), as well as the work of
scientists based in Europe such as Peter Allen (1997) and Brian Goodwin (Goodwin
1995, Webster & Goodwin 1996); Axelrod on cooperation (Axelrod 1990, 1997;
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Axelrod & Cohen 2000); Casti (1997), Bonabeau et al (1999), Epstein & Axtel (1996)
and Ferber (1999) on modelling and computer simulation; work by Ilya Prigogine
(Prigogine & Stengers 1985, Nicolis & Prigogine 1989, Prigogine 1990), Isabelle
Stengers (Prigogine & Stengers 1985), Gregoire Nicolis (Nicolis & Prigogine 1989,
Nicolis 1994) on dissipative structures; work by Humberto Maturana, Francisco Varela
(Varela & Maturana 1992) and Niklaus Luhman (1990) on autopoiesis (Mingers 1995);
as well as the work on chaos theory (Gleick 1987) and that on economics and increasing
returns by Brian Arthur (1990, 1995, 2002).
The above can be summarised as five main areas of research on (a) complex
adaptive systems at SFI and Europe; (b) dissipative structures by Ilya Prigogine and his
co-authors; (c) autopoiesis based on the work of Maturana in biology and its application
to social systems by Luhman; (d) chaos theory; and (e) increasing returns and path
dependence by Brian Arthur and other economists (e.g. Hodgson 1993, 2001). Fig.1
shows the five main areas of research that form the background to this chapter and the
ten generic principles of complexity that will be discussed. Since the ten principles
incorporate more than the work on complex adaptive systems (CAS), the term complex
evolving systems (CES) will be used (Allen) as more appropriate to this discussion.
By comparison with the natural sciences there was relatively little work on
developing a theory of complex social systems despite the influx of books on
complexity and its application to management in the past 6-7 years (an extensive review
of such publications is given by Maguire & McKelvey 1999). The notable exceptions
are the work of Luhman on autopoiesis, Arthur in economics and the work on strategy
by Lane & Maxfield (1997), Parker & Stacey (1994) and Stacey (1995, 1996, 2000,
2001). A theory in this context is interpreted as an explanatory framework that helps us
understand the behaviour of a complex social (human) system. (The focus of the
author’s work and hence the focus of this chapter is on human organisations. Other
researchers have concentrated on non-human social systems, such as bees, ants, wasps,
etc.) Such a theory may provide a different way of thinking about organisations, and
could change strategic thinking and our approach to the creation of new organisational
forms—that is, the structure, culture, and technology infrastructure of an organisation. It
may also facilitate, in a more modest way, the emergence of different ways of organising
within a limited context such as a single department within a firm. The case study at the
end of this chapter describes how a different way of organising emerged in the
Information Technology Department in the London office of an international bank.
The chapter will discuss each principle in turn, providing some of the scientific
background and describing in what way each principle may be relevant and appropriate
to a human system. Regarding the five areas of research listed on the left hand side of
Figure 1, dissipative structures are discussed at length as part of the ‘far-from-
equilibrium’ and ‘historicity’ principles; complex adaptive systems research underlies
most of the other principles and the work of Kauffman is referred to extensively;
autopoiesis is not discussed in this chapter but it has played an important role in the
thinking underlying the current work (for the implications and applications of
autopoiesis see Mingers 1995); chaos theory is given a separate section, but the
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discussion is not extensive; and Arthur’s work on increasing returns is discussed under
the ‘path-dependence’ principle.
Theories
Natural sciences
Dissipative structures
chemistry-physics (Prigogine)
Complex Adaptive Systems
evolutionary biology (Kauffman)
Autopoiesis (self-generation)
biology/cognition (Maturana)
Chaos theory
Social sciences
Increasing returns
economics (B. Arthur)
self-organisation
emergence
connectivity
interdependence
feedback
far from equilibrium
space of possibilities
co-evolution
historicity & time
path-dependence
creation of new order
Generic
characteristics
of complex
adaptive
systems
Figure 1
The four principles grouped together in Fig. 1, of emergence, connectivity,
interdependence, and feedback are familiar from systems theory. Complexity builds on
and enriches systems theory by articulating additional characteristics of complex
systems and by emphasising their inter-relationship and interdependence. It is not
enough to isolate one principle or characteristic such as self-organisation or emergence
and concentrate on it in exclusion of the others. The approach taken by this chapter
argues for a deeper understanding of complex systems by looking at several
characteristics and by building a rich inter-related picture of a complex social system. It
is this deeper insight that will allow strategists to develop better strategies and
organisational designers to facilitate the creation of organisational forms that will be
sustainable in a constantly changing environment.
The discussion is based on generic principles, in the sense that these principles or
characteristics are common to all natural complex systems. One way of looking at
complex human systems is to examine the generic characteristics of natural complex
systems and to consider whether they are relevant or appropriate to social systems. But
there is one limitation in that approach, which is to understand that such an examination
is merely a starting point and not a mapping, and that social systems need to be studied
in their own right.
This limitation is emphasised for two reasons: (a) although it is desirable that
explanation in one domain is consistent with explanation in another and that these
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explanations honour the Principle of Consistency (Hodgson 2001, p90), characteristics
and behaviour cannot be mapped directly from one domain to another, without a
rigorous process of testing for appropriateness and relevance. Not only may the unit of
analysis be quite different, but scientific and social domains may also have certain
fundamental differences that may invalidate direct mapping. For example humans have
the capacity to reflect and to make deliberate choices and decisions among alternative
paths of actions. This capacity may well distinguish human behaviour from that of
biological, physical or chemical entities; (b) a number of researchers consider the
principles of complexity only as metaphors or analogies when applied to human
systems. But metaphors and analogies are both limiting and limited and do not help us
understand the fundamental nature of a system under study. This does not mean that
neither metaphor nor analogy may be used. We use them as ‘transitional objects’ all the
time in the sense that they help the transition in our thinking when faced with new or
difficult ideas or concepts. The point being emphasised, is that using metaphor and
analogy is not the only avenue available to us in understanding complexity in an
organisational or broader social context. Since organisations are, by their very nature,
complex evolving systems, they need to be considered as complex systems in their own
right.
Another way of looking at complexity is that suggested by Nicolis and Prigogine
(1989 p8) “It is more natural, or at least less ambiguous, to speak of complex behavior
rather than complex systems. The study of such behavior will reveal certain common
characteristics among different classes of systems and will allow us to arrive at a proper
understanding of complexity.” This approach both honours the Principle of Consistency
and avoids the metaphor debate. It may however upset some sociologists who do not
find ‘arguments from science’ convincing. But this is to miss Nicolis’s and Prigogine’s
point, when they put the emphasis on the behaviour or characteristics of all complex
systems. Nicolis and Prigogine are not behaviourists; they study the behaviour of
complex systems in order to understand their deeper, essential nature.
This provides us with the underlying reason for studying complexity. It explains and
thus helps us to understand the nature of the world—and the organisations—we live in.
The term ‘complexity’ will be used to refer to the theories of complexity (in the
literature the plural ‘theories’ is reduced to the singular for ease of reference and this
practice will be used here) and ‘complex behaviour’ to the behaviour that arises from the
interplay of the characteristics or principles of complex systems.
Complexity is not a methodology or a set of tools (although it does provide both). It
certainly is not a ‘management fad’. The theories of complexity provide a conceptual
framework, a way of thinking, and a way of seeing the world.
1. Connectivity & Interdependence
Complex behaviour arises from the inter-relationship, interaction, and inter-
connectivity of elements within a system and between a system and its environment.
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Murray Gell-Mann (1995/96) traces the meaning to the root of the word. Plexus means
braided or entwined, from which is derived complexus meaning braided together, and
the English word “complex” is derived from the Latin. Complex behaviour therefore
arises from the intricate inter-twining or inter-connectivity of elements within a system
and between a system and its environment.
In a human system, connectivity and interdependence means that a decision or action
by any individual (group, organisation, institution, or human system) may affect related
individuals and systems. That affect will not have equal or uniform impact, and will vary
with the ‘state’ of each related individual and system, at the time. The state’ of an
individual or a system will include its history and its constitution, which in turn will
include its organisation and structure. Connectivity applies to the inter-relatedness of
individuals within a system, as well as to the relatedness between human social systems,
which include systems of artefacts such as information technology (IT) systems and
intellectual systems of ideas.
Complexity theory, however, does not argue for ever-increasing interconnectivity,
for high connectivity implies a high degree of interdependence. This means that the
greater the interdependence between related systems or entities the wider the ‘ripples’ of
perturbation or disturbance of a move or action by any one entity on all the other related
entities. Such high degree of dependence may not always have beneficial effects
throughout the ecosystem. When one entity tries to improve its fitness or position, this
may result in a worsening condition for others. Each ‘improvement’ in one entity
therefore may impose associated ‘costs’ on other entities, either within the same system
or on other related systems.
Connectivity and interdependence is one aspect of how complex behaviour arises.
Another important and closely related aspect is that complex systems are
multidimensional, and all the dimensions interact and influence each other. In a human
context the social, cultural, technical, economic and global dimensions may impinge
upon and influence each other. The case study at the end of the chapter, illustrates how
what on the surface appeared to be a technical problem involving the integration of
information systems across Europe, was partially resolved by paying attention to some
social and cultural issues.
But the distinguishing characteristic of a CES is that it is able to adapt and evolve
and thus create new order and coherence. This creation of new order and coherence is
one of the key defining features of complexity. Individuals acting ‘at random’ or with
their own agendas nevertheless can work effectively as a group or an entire
organisation—and may create coherence in the absence of any grand design. They can
also create new ways of working, new structures, and different relationships, where
hierarchies may be reversed or ignored, as in integrated project teams1 where a senior
1 Integrated project/product teams (IPTs) are often used in the Aerospace and other industries to bring
together representatives from different organisations or functions with the knowledge and skills necessary
to design a new project or product.
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executive outside the team may not hold a leadership role within the team, while a more
junior employee becomes team-leader because he/she has the correct qualifications for
leading that particular integrated project team.
Other features include the possibility of entities in a CES to change their rules of
interaction; to act on limited local knowledge, without knowing what the system as a
whole is doing; and to be self-repairing and self-maintaining. Reference to entities as
individuals or collections (systems) is deliberately ambiguous, to emphasise the point
that complex characteristics tend to be scale-invariant and could apply at all scales from
an individual to a whole system as well as to systems at different scales (e.g. team,
organisation, industry, economy, etc.)
1.1 Degrees of Connectivity
Propagation of influence through an ecosystem depends on the degree of
connectivity and interdependence. Biological “ecosystems are not totally connected.
Typically each species interacts with a subset of the total number of other species, hence
the system has some extended web structure.” (Kauffman 1993: 255) In human social
ecosystems the same is true. There are networks of relationships with different degrees
of connectivity. Degree of connectivity means strength of coupling and the dependencies
known as epistatic interactions—i.e. the extent to which the fitness contribution made
by one individual depends on related individuals. In biological co-evolutionary
processes, the fitness of one organism or species depends upon the characteristics of the
other organisms or species with which it interacts, while all simultaneously adapt and
change. (Kauffman 1993, 33) In other words a single entity (allele, gene, organism or
species) does not contribute to overall fitness independently of all other like entities. The
fitness contribution of an individual may depend on all the other individuals in that
context. This is a contextual measure of dependency, of direct or indirect influence that
each entity has on those it is coupled with.
In a social context, each individual belongs to many groups and different contexts
and his/her contribution in each context depends partly on the other individuals within
that group and the way they relate to the individual in question. An example is when a
new member joins a team. The contribution that individual will be allowed to make to
that team may depend on the other members of the team and on the space they provide
for such a contribution, as much as to the skills, knowledge, expertise, etc brought by the
new member.
In human systems, connectivity between individuals or groups is not a constant or
uniform relationship, but varies over time, and with the diversity, density, intensity, and
quality of interactions between human agents. Connectivity may also be formal or
informal, designed or undesigned, implicit with tacit connections or explicit.
Furthermore, it is the degree of connectivity, which determines the network of
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relationships and the transfer of information and knowledge and is an essential element
in feedback processes.
2. Co-evolution
Connectivity applies not only to elements within a system but also to related systems
within an ecosystem. An ecosystem in biology means, “each kind of organism has, as
parts of its environment, other organisms of the same and of different kinds ... adaptation
by one kind of organism alters both the fitness and the fitness landscape2 of the other
organisms” (Kauffman 1993, p242) The way each element influences and is in turn
influenced by all other related elements in an ecosystem is part of the process of co-
evolution which Kauffman describes as “a process of coupled, deforming landscapes
where the adaptive moves of each entity alter the landscapes of its neighbors.”
(Kauffman & Macready, 1995)
Another way of describing co-evolution is that the evolution of one domain or entity
is partially dependent on the evolution of other related domains or entities (Ehrlich &
Raven 1964, Pianka 1994, Kauffman 1993 & 1995, McKelvey 1999a & b, Koza &
Lewin 1998); or that one domain or entity changes in the context of the other(s). The
notion of co-evolution places the emphasis on the evolution of interactions and on
reciprocal evolution (Futuyama 1979). In human systems, co-evolution in the sense of
the evolution of interactions places emphasis on the relationship between the co-
evolving entities.
A point emphasised by Kauffman is that co-evolution takes place within an
ecosystem, and cannot happen in isolation. In a human context a social ecosystem
includes the social, cultural, technical, geographic and economic dimensions and co-
evolution may affect both the form of institutions and the relationships and interactions
between the co-evolving entities (the term entity is used as a generic term which can
apply to individuals, teams, organisations, industries, economies, etc.).
A distinction may also be made between co-evolution with and adaptation to a
changing environment. When the emphasis is placed on co-evolution with, it tends to
change the perspective and the assumptions that underlie much traditional management
and systems theories.
Although we make a conceptual distinction between a ‘system’ and its
‘environment’ it is important to note that there is no dichotomy or hard boundary
between the two as in Figure 2, in the sense that a system is separate from and always
adapts to a changing environment. The notion to be explored is rather that of a system
closely linked with all other related systems within an ecosystem, illustrated by Figure 3.
2 Kauffman (1993, p33) borrows the hill-climbing framework with minor modifications, directly from
Wright (1931, 1932) who introduced the concept of a space of possible genotypes. Each genotype has a
‘fitness’, and the distribution of fitness values over the space of genotypes constitutes a fitness landscape.
Depending upon the distribution of the fitness values, the fitness landscape can be more or less
mountainous.
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Within such a context change needs to be seen in terms of co-evolution with all other
related systems, rather than as adaptation to a separate and distinct environment. This
perspective changes the way strategy may be viewed.
Figure 2
Co-evolution within an ecosystem
Figure 3
Figure 3
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In a social co-evolving ecosystem, each organisation is a fully participating agent
which both influences and is influenced by the social ecosystem made up of all related
businesses, consumers, and suppliers, as well as economic, cultural, and legal
institutions. Strategies consequently cannot to be seen simply as a response to a
changing environment, which is separate from the organisation, but as adaptive moves,
which will affect both the initiator of the action and all others influenced by it. The
notion of co-evolution is thus one of empowerment, as it suggests that all actions and
decisions affect the social ecosystem. No individual or organisation is powerless—as
each entity’s actions reverberate through the intricate web of inter-relationships and
affects the social ecosystem. But co-evolution also invites notions of responsibility, as
once the ecosystem is influenced and affected it will in turn affect the entities
(individuals, organisations, and institutions) within it. This notion is not the same as pro-
active or re-active response. It is a subtler ‘sensitivity’ and awareness of both changes in
the environment and the possible consequences of actions. It argues for a deeper
understanding of reciprocal change and the way it affects the totality.
Seen from one perspective, co-evolution takes place when related entities change at
the same time. But in most observable examples it is more a matter of short-term
adaptation and long-term co-evolution. Two examples will be used to illustrate this. The
first example was given by Maturana at an Open University workshop (Maturana 1997).
When I buy a pair of shoes, both the new shoes and my feet will change to accommodate
each other. They co-evolve. What I observe at a macro-level after wearing the shoes
several times and suffering from sore feet, may be co-evolution happening at the same
time, as both my feet and shoes change to accommodate each other. But at a micro
short-term level of minute-to-minute walking, there could well have been short-term
adaptation of the one to the other. This reciprocal movement is illustrated more clearly
by the second example given by a senior Marks & Spencer executive at an LSE
Seminar. Weavers and knitters have influenced each other and produced new materials,
which are knitted but look woven, and materials that are woven but look knitted. They
have co-evolved over time, with short-term adaptation to each other and the market.
Through the process of co-evolution they have produced something new, a new order or
coherence; which is, as has been pointed out earlier, the key distinguishing feature of
CES.
Co-evolution also happens between entities within a system, and the rate of their co-
evolution (McKelvey 1999b) is worth considering. For example, how can the rate of co-
evolution within and between teams be facilitated and improved? Co-evolution in this
context is associated with learning and the transfer of information and knowledge. If one
individual or one team learns to operate better, how can that knowledge or ability be
transferred to other teams to help them evolve? Since co-evolution can only take place
within an ecosystem, the notion of social ‘ecosystem’ also needs to be addressed. An
ecosystem is defined by the interdependence of all entities within it. It provides
sustenance and support for life. A community is a social ecosystem, if it provides mutual
support and sustenance. When firms and institutions cease to function like a community
or social ecosystem, they may break down. Some of the most successful organisations
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nurture their community or social ecosystem. (Lewin & Regine date?) The debate on
organisational culture is attempting to address that issue. How can the organisation
create the kind of culture that will help it to survive and thrive? Or what are the
conditions that will help it co-create a sustainable social ecosystem?
Co-evolution therefore affects both individuals and systems and is operational at
different levels, scales, or domains. Co-evolution is taking place at all levels and scales
and can be thought of as endogenous co-evolution when it applies to individuals and
groups within the organisation and as exogenous co-evolution when the organisation is
interacting with the broader ecosystem. This however is a simplification—as the
endogenous and exogenous processes are necessarily interlinked and the boundaries
between the organisation and its ‘environment’ may not be clear-cut and stable.
Furthermore the notion of ‘ecosystem’ applies both within the organisation and to the
broader environment, which includes the organisation under study. Hence the notion of a
complex co-evolving ecosystem is one of intricate and multiple intertwined interactions
and relationships, and of multi-directional influences and links, both direct and many-
removed. Connectivity and interdependence propagates the effects of actions, decisions
and behaviours throughout the ecosystem, but that propagation or influence is not
uniform as it depends on the degree of connectivity.
3. Dissipative Structures, Far-from-equilibrium & History
Another key concept in complexity is dissipative structures, which are ways in which
open systems exchange energy, matter, or information with their environment and which
when pushed ‘far-from-equilibrium’ create new structures and order.
The Bénard cell is an example of a physico-chemical dissipative structure. It is made
up of two parallel plates and a horizontal liquid layer, such as water. The dimensions of
the plates are much larger than the width of the layer of water. When the temperature of
the liquid is the same as that of the environment, the cell is at equilibrium and the fluid
will tend to a homogeneous state in which all its parts are identical (Nicolis &
Prigogine1989, Prigogine & Stengers 1985). If heat is applied to the bottom plate, and
the temperature of the water is greater at the bottom than at the upper surface, at a
threshold temperature the fluid becomes unstable. “By applying an external constraint
we do not permit the system to remain at equilibrium.” (Nicolis & Prigogine 1989, p10)
If we remove the system farther and farther from equilibrium by increasing the
temperature differential, suddenly at a critical temperature the liquid performs a bulk
movement which is far from random: the fluid is structured in a series of small
convection ‘cells’ known as Bénard cells.
Several things have happened in this process: (a) the water molecules have
spontaneously organised themselves into right-handed and left-handed cells. This kind
of spontaneous movement is called self-organisation and is one of the key
characteristics of complex systems; (b) from molecular chaos the system has emerged as
a higher-level system with order and structure; (c) the system was pushed far-from-
equilibrium by an external constraint or perturbation; (d) although we know that the
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cells will appear, “the direction of rotation of the cells is unpredictable and
uncontrollable. Only chance in the form of the particular perturbation that may have
prevailed at the moment of the experiment, will decide whether a given cell is right- or
left-handed.” (Nicolis & Prigogine 1989, p14); (e) when a constraint is sufficiently
strong, the system can adjust to its environment in several different ways, that is several
solutions are possible for the same parameter values; (f) the fact that only one among
many possibilities occurred gives the system “a historical dimension, some sort of
“memory” of a past event that took place at a critical moment and which will affect its
further evolution.” (Nicolis & Prigogine 1989, p14); (g) the homogeneity of the
molecules at equilibrium was disturbed and their symmetry was broken3; (h) the
particles behaved in a coherent manner, despite the random thermal motion of each of
them. This coherence at a macro level characterises emergent behaviour, which arises
from micro-level interactions of individual elements.
In the Bénard cell heat transfer has created new order. It is this property of complex
systems to create new order and coherence that is their distinctive feature. The Bénard
cell process in thermal convection is the basis of several important phenomena, such as
the circulation of the atmosphere and oceans that determines weather changes. (Nicolis
& Prigogine1989, p8)
Ilya Prigogine was awarded the 1977 Nobel Prize for chemistry for his work on
dissipative structures and his contributions to nonequilibrium thermodynamics.
Prigogine has reinterpreted the Second Law of Thermodynamics. Dissolution into
entropy is not an absolute condition, but “under certain conditions, entropy itself
becomes the progenitor of order.” To be more specific, “... under non-equilibrium
conditions, at least, entropy may produce, rather than degrade, order (and) organisation
... If this is so, then entropy, too, loses its either/or character. While certain systems run
down, other systems simultaneously evolve and grow more coherent.” (Prigogine &
Stengers 1985: xxi)
Symmetry breaking in complexity means that the homogeneity of a current order is
broken and new patterns emerge. Symmetry breaking may be understood as a generator
of information, in the sense that when a pattern of homogeneous data is broken by
differentiated patterns, the new patterns can be read as ‘information’. This phenomenon
applies to and can be interpreted at different levels, from undifferentiated code
(homogeneous data) to exception reporting, when different or unexpected patterns
appear to deviate from the expected norms.
In dissipative structures the tendency to split into alternative solutions is called
bifurcation, but the term is misleading in that it means a separation into two paths, when
there may be several possible solutions. However, as it is easier to explain the splitting
of possibilities into two alternative paths, this simplified meaning will be used, with the
proviso that multiple solutions are also possible. In the Bénard cell, a unique solution is
3 “The emergence of the concept of space in a system in which space could not previously be perceived in
an intrinsic manner is called symmetry breaking.” Nicolis & Prigogine, 1989 p12
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present until the heat differential reaches a critical value. At that point the molecules
self-organise themselves and become right- or left-handed cells. The two possibilities
are present simultaneously. Figure 4 is borrowed from Nicolis and Prigogine (1989 p72)
and illustrates bifurcation.
An observer could not predict which state will emerge; “only chance will decide,
through the dynamics of fluctuations. The system will in effect scan the territory and
will make a few attempts, perhaps unsuccessful at first, to stabilize. Then a particular
Unique
Solution
Thermodynamic
branch
(a)
Stable
c
Unstable
(a')
(b1)
(b2)
Stable
Stable
X
Figure 4
(from Nicolis & Prigogine, 1989, p72)
Bifurcation diagram showing how a state variable X is affected when the control parameter varies. a unique
solution (a), the thermodynamic branch, loses its stability at c. At this value of the control parameter new
branches of solutions (b1, b2 ), which are stable at the example shown, are generated.
13
fluctuation will take over. By stabilizing it the system becomes a historical object in the
sense that its subsequent evolution depends on this critical choice.” (Nicolis &
Prigogine, 1989 p72) At a totally different scale, the notions of chance and history are
used by Kauffman to describe a view of evolutionary biology that sees “… organisms as
ultimately accidental and evolution as an essentially historical science. In this view, the
order in organisms results from selection sifting unexpected useful accidents and
marshalling them into improbable forms. In this view, the great universals of biology—
the genetic code, the structure of metabolism and others—are to be seen as frozen
accidents, present in all organisms only by virtue of shared descent.” (Kauffman 1993,
pxv)
In a social context, it is the series of critical decisions each individual takes from
several possible alternatives that may determine a particular life path for that individual.
The alternatives available, however, are constrained by the person’s current state and the
state of the landscape the person occupies. Thus the emergent behaviour of the person is
not a matter of ‘chance’ but is the result of a person’s selection among a finite set of
perceived choices; as well as the past choices made (the history) that have shaped that
person’s life path. Once the decision is made, there is a historical dimension and
subsequent evolution may depend on that critical choice; but before the decision is
finalised, the alternatives are sources of innovation and diversification, since the opening
up of possibilities endows the individual and the system with new solutions. When a
social entity (individual, group, organisation, industry, economy, country, etc) is faced
with a constraint, it finds new ways of operating, because away-from-equilibrium
(established norms) systems are forced to experiment and explore their space of
possibilities, and this exploration helps them discover and create new patterns of
relationships and different structures.
Non-equilibrium may allow a system to avoid thermal disorder and to transform part
of the energy communicated from the environment into an ordered behaviour of a new
type, a new dissipative structure that is characterised by symmetry breaking and
multiple choices. In chemistry, autocatalysis (the presence of a substance may increase
the rate of its own production) shows similar behaviours, and the Belousov-Zhabotinski
(BZ) reaction, under certain non-equilibrium conditions shows symmetry breaking, self-
organisation, multiple possible solutions, and hysteresis (the specific path of states that
can be followed depends on the system’s past history). (Nicolis & Prigogine 1989,
Kauffman 1993, 1995) Furthermore, self-reproduction, a fundamental property of
biological life, is “the result of an autocatalytic cycle in which the genetic material is
replicated by the intervention of specific proteins, themselves synthesized through the
instructions contained in the genetic material.” (Nicolis & Prigogine 1989, p 18) In one
sense, complexity is concerned with systems in which evolution—and hence history—
plays or has played an important role, whether biological, physical, or chemical systems.
Similarly in a social context, when an organisation moves away from equilibrium
(i.e. from established patterns of work and behaviour) new ways of working are created
and new forms of organisation may emerge. These may be quite innovative if choice is
allowed and the symmetry of established homogeneous patterns is broken. There is
14
however a fundamental difference between natural and social human systems. The latter
can deliberately create constraints and perturbations that consciously push a human
institution far-from-equilibrium. In addition, humans can also provide help and support
for a new order to be established. If the new order is ‘designed’ in detail, then the
support needed will be greater, because those involved have their self-organising
abilities curtailed, and may thus become dependent on the designers to provide a new
framework to facilitate and support new relationships and connectivities. Although the
intention of change management interventions is to create new ways of working, they
may block or constrain emergent patterns of behaviour if they attempt to excessively
design and control outcomes. However, if organisation re-design were to concentrate on
the provision of enabling infrastructures (the socio-cultural and technical conditions that
facilitate the emergence of new ways of organising), allowing the new patterns of
relationships and ways of working to emerge, new forms of organisation may arise that
would be unique and perhaps not susceptible to copying. These new organisational
forms may be more robust and sustainable in competitive environments.
4. Exploration-of-the-space-of- possibilities
Complexity suggests that to survive and thrive an entity needs to explore its space of
possibilities and to generate variety. Complexity also suggests that the search for a
single 'optimum' strategy may neither be possible nor desirable. Any strategy can only
be optimum under certain conditions, and when those conditions change, the strategy
may no longer be optimal. To survive an organisation needs to be constantly scanning
the landscape and trying different strategies. An organisation may need to have in place
several micro-strategies that are allowed to evolve before major resources are committed
to a single strategy. This reduces the risk of backing a single strategy too early, which
may turn out not to be the best one, and supports sensitive co-evolution with a changing
ecosystem. In essence, unstable environments and rapidly changing markets require
flexible approaches based on requisite variety. (Ashby 1969)
Flexible adaptation also requires new connections or new ways of seeing things.
Seeing a novel function for a part of an existing entity is called exaptation’4. A small
example might help explain the concept. While on holiday, I was using my laptop
computer in the garden. The computer was on a garden table, with a hole in the middle
for an umbrella. The laptop was connected to a mobile telephone, which enabled me to
send and receive emails and faxes. Both the computer and the mobile were attached to
power leads, which were passed through a window into the house. The plethora of leads
was both ugly and fragile, as people passing by could trip over them. They also took up
a lot of space on the table. My son Daniel then used the hole in the middle of the table to
keep the leads tidy and out of sight. The umbrella hole therefore gained a novel function,
4 ‘Exaptation is the term used by Stephen J.Gould and Stuart Kauffman. Darwin used the term
‘preadaptation’. “Darwin noted that in an appropriate environment a causal consequence of a part of an
organism that had not been of selective significance might come to be of selective significance and hence
be selected. Thereupon, that newly important causal consequence would be a new function available to the
organism.” Evolutionary adaptations “by such preadaptations, or exaptations, are not rare; they are the
grist of adaptive evolution. Thus arose the lung, the ear, flight.” (Kauffman, 2000, p130)
15
in keeping the leads tidy and safe. That simple solution was an example of an exaptation.
Daniel ‘saw’ the different function for the umbrella hole, while no one else had even
considered it.
When searching the space of possibilities, whether for a new product or a different
way of doing things, it is not possible to explore all possibilities. It may, however, be
possible to consider change one step away from what already exists. In this sense,
exaptation may be considered an exploration of what is sometimes called the ‘adjacent
possible’. (Kauffman 2000) That is exploring one step away, using ‘building blocks’
already available, but put together in a novel way. According to Kauffman (2000, p22)
the push into novelty in the molecular, morphological, behavioural, technological and
organisational spheres, is persistent and happens through exploration of the adjacent
possible. The rate of discovery or mutation, however, is restricted by selection to avoid
possible catastrophes that could destroy a community. Bacteria and higher cells have a
mutation rate well below the error-catastrophe, which is the phase transition that renders
a population unsustainable. There seems to be a balance between discovery and what the
ecosystem can effectively sustain. Both the biosphere and the econosphere seem to have
“endogenous mechanisms that gate the exploration of the adjacent possible such that, on
average, such explorations do successfully find new ways of making a living.”
(Kauffman 2000, p156) In the biosphere adaptations are selected by natural selection
and in the econosphere by economic success or failure, at a rate that is sustainable. The
current slowing down in the mobile telephone market, could well be an indicator of
intolerance to the rate of innovation, which cannot be assimilated by the market.
Although the rate at which novelty can be introduced is restricted, the adjacent
possible is indefinitely expandable. (Kauffman 2000, p142) Once discoveries have been
realised in the current adjacent possible, a new adjacent possible, accessible from the
enlarged actual that includes the novel discoveries from the former adjacent possible,
becomes available. The constant opening up of niche markets in areas and products that
only a few years earlier had not even been thought of, is an example of the ever
expanding possibilities of the adjacent possible.
5. Feedback
Feedback is traditionally seen in terms of positive and negative feedback
mechanisms, which are also described as “reinforcing (i.e. amplifying) and balancing.”
(Kahen & Lehman, http://www-dse.doc.ic.ac.uk/~mml/). Putting it another way, positive
(reinforcing) feedback drives change, and negative (balancing, moderating, or
dampening) feedback maintains stability in a system. A familiar example of negative
feedback is provided in a central heating system. A thermostat monitors the temperature
in the room, and when the temperature drops below a specified level, an adjusting
mechanism is set in motion, which turns the heating on until the desired temperature is
attained. Similarly, when the temperature rises above a set norm, the heating is switched
off until the temperature falls below the desired level. The gap between the desired and
the actual temperature is thus closed. Positive feedback, on the other hand, would
16
progressively widen the gap. Instead of reducing or cancelling out the deviation, positive
feedback would amplify it.
One point needs to be made. First, feedback ‘mechanisms’ are related to engineering
and other machine-type systems, as indicated by the language used (e.g. ‘adjustment
mechanism’). When feedback is applied to human systems, the term feedback process
will be used, in an attempt to avoid the machine metaphor and to distinguish human
from other complex systems.
In far-from-equilibrium conditions, non-linear relationships prevail, and a system
becomes “inordinately sensitive to external influences. Small inputs yield huge, startling
effects” (Prigogine & Stengers 1985: xvi) that cause a whole system to reorganise itself.
Part of that process is likely to be the result of positive or reinforcing feedback. “In far-
from-equilibrium conditions we find that very small perturbations or fluctuations can
become amplified into gigantic, structure-breaking waves.” (Prigogine & Stengers 1985:
xvii)
In human systems, far-from-equilibrium conditions operate when a system is
perturbed well away from its established norms, or away from its usual ways of working
and relating. When an organisation as a system is thus disturbed (e.g. after restructuring
or a merger), it may reach a critical point and either degrade into disorder (loss of
morale, loss of productivity, etc.) or create some new order and organisation—i.e. find
new ways of working and relating—and thus create a new coherence. Positive or
reinforcing feedback processes underlie such transformation and they provide a starting
point for understanding the constant movement between change and stability in complex
systems.
One reason for interventions that create far-from-equilibrium conditions may be that
the current feedback processes no longer work. This may be the case when negative or
balancing feedback processes that once were able to adjust or influence the behaviour of
the organisation can no longer produce the desired outcome. When efforts to improve
behaviour in order to improve performance and market position continually fail, and
when incremental changes are no longer effective, then managers of organisations may
resort to major interventions in an effort to produce radical change. These interventions
may also fail, however, and an organisation may become locked in a constant cycle of
ineffective restructuring. One reason for such failures is over-reliance on ‘adjustment
mechanisms’ based on negative feedback loops that have worked in the past. But in a
turbulent environment, the entire ecosystem may be changing, and we cannot always
extrapolate successfully from past experience. New patterns of behaviour and new
structures may need to emerge, and these may depend on or become established through
new positive feedback processes.
In human systems, the degree of connectivity (dependency or epistatic interaction)
often determines the strength of feedback. Feedback when applied to human interactions
means influence that changes potential action and behaviour. Furthermore, in human
interactions feedback is rarely a straightforward input–process–output procedure with
17
perfectly predictable and determined outputs. Actions and behaviours may vary
according to the degree of connectivity between different individuals, as well as with
time and context.
Co-evolution may also depend on reciprocal feedback influences between entities.
An important question is therefore, how does degree of connectivity and feedback
influence co-evolution? A related question is, how does the structure of an ecosystem
affect co-evolution? Kauffman makes the bold statement that “We have found evidence
... that the structure of an ecosystem governs co-evolution.” (Kauffman 1993: 279) This
statement is based on computer simulations, but it is intuitively appealing and there is
evidence that this finding may apply to social ecosystems (LSE Complexity
Programme). Feedback processes may therefore have a bearing on degree of
connectivity (at all levels), hence on ecosystem structure, and hence on co-evolution.
Furthermore, the two simple concepts of positive and negative feedback need to be
elaborated in order to describe the multiple interacting feedback processes in complex
systems, and we need to rethink the nature of feedback in this context to recognise
multi-level, multi-process, non-linear influences.
5.1 Path Dependence & Increasing Returns
Brian Arthur argues that conventional economic theory is based on the implicit
assumption of negative feedback loops in the economy, which lead to diminishing
returns, which in turn lead to (predictable) equilibrium outcomes. Negative feedback has
a stabilising effect, and implies a single equilibrium point, as “any major changes are
offset by the very reactions they generate” (Brian Arthur, 1990 p92). The example given
by Arthur is the high oil prices of the 1970s, which encouraged energy conservation and
increased oil exploration, precipitating a predictable increase in supply and resulting
drop in prices by the early 1980s. But, Arthur argues, such stabilising forces do not
always operate or dominate. “Instead positive feedback magnifies the effects of small
economic shifts”, and increasing returns from positive feedback makes for many
possible equilibrium points, depending on the negative feedback loops that may also
operate in a system (Arthur 1990).
The possibility that a system may have more than one possible equilibrium points
has also been described in section 3 under dissipative structures. In physico-chemical
systems “two (or sometimes several) simultaneously stable states could coexist under the
same boundary conditions.” Nicolis and Prigogine call this phenomenon ‘bistability
and describe it as “the possibility to evolve, for given parameter values, to more than one
stable state” (Nicolis & Prigogine, 1989, p24). Furthermore, the specific paths that a
system may follow depend on its past history. The point here is that past history affects
future development, and there may be several possible paths or patterns that a system
may follow. This explains why the precise behaviour of a complex system may be very
difficult to predict, even while keeping the system within certain bounds.
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The classic example illustrating Arthur’s argument of increasing returns (Arthur
1990, 1995) resulting from a virtuous circle of self-reinforcing growth is the
videocassette recorder. “The VCR market started out with two competing formats
selling at about the same price: VHS and Beta. Each format could realise increasing
returns as its market share increased: large numbers of VHS recorders would encourage
video outlets to stock more pre-recorded tapes in VHS format, thereby enhancing the
value of owning a VHS recorder and leading more people to buy one. (The same would,
of course, be true for Beta-format players.) In this way, a small gain in market share
would improve the competitive position of one system and help it further increase its
lead. ... Increasing returns on early gains eventually tilted the competition toward VHS:
it accumulated enough of an advantage to take virtually the entire VCR market.”
(Arthur, 1990) This process is what Arthur calls ‘path dependence’ - the increasing pull
of a new technology in attracting or enabling further developments. The more associated
products (e.g. pre-recorded tapes) and support services (shops selling tapes in VHS
format; selling VHS recorders; engineers becoming available to service the recorders,
etc) proliferated, the stronger the position of the VHS format became, until it dominated
the market.
Other technical standards or conventions established by positive feedback,
increasing returns and path dependence, are the gauge of railway tracks, the English
language becoming established as the standard language of air navigation and a
particular screw thread, which often “cannot be changed even if alternative techniques or
conventions may be better” (Mainzer, 1996, p271).
The story however is not as simple as it may appear and the process leading to
increasing returns and path dependence is not straightforward. Positive feedback is not
the only process in operation in the examples given above. Apart from reinforcing
feedback loops, there are negative feedback or stabilising loops also in operation. The
two processes may be present simultaneously or they may follow each other as the
market progresses through various economic cycles. Markets and economies are
complex systems that co-evolve, are dissipative (in the sense that they are irreversible
and have a history), show self-organisation and emergence, and explore their space of
possibilities. As all these characteristics play out, the progression of any technology or
market is not smooth.
Arthur in later studies (Arthur 2002) looks closely at the development of technology
clusters (e.g. with electrification come dynamos, generators, transformers, switchgear,
power distribution systems; with mass production and the automobile come production
lines, modern assembly methods, ‘scientific management’ road systems, oil refineries,
traffic control), which have defined “an era, an epoch, a revolution” (Arthur 2002). He
shows how they eventually change the way business is done, and that they may even
change the way society is conducted. The process starts with one or more technologies
that ‘enable’ the new cluster (Perez 2002). The new technology cluster may at first
attract little notice, but then starts to achieve successes in early demonstrations and small
companies may be set up based on the new ideas. These compete intensely at this early
turbulent phase and as successes increase, and Government regulation is mainly absent,
19
the promise of large profits becomes apparent and the public may start to speculate. In
certain cases this first exuberant phase is marked by a crash, and Arthur cites three
examples, the railway industry crash in the UK in 1847; the Canal Mania of the 1790s
with the shares crashing in 1793; and the recent Internet crash. In the past, the crash was
followed by a sustained build-out or golden age of the technology, which influenced
growth in the economy and the period was one of confidence and prosperity, like the
period after 1850 in the UK when the railways became “the engine of the economy in
Britain” (Arthur 2002). The last phase is one of maturity.
The point that Arthur is trying to make in this study is to show that if we take a
historical perspective and compare the railways to the Internet then the real benefits are
yet to come. While building his argument, however, he also shows the constant interplay
between positive and negative feedback loops moving the markets between periods of
expansion and stability. The story also illustrates co-evolution in the economy,
exploration, the adjacent possible, and the emergence of new order.
6. Self-organisation, Emergence and the Creation of New Order
Self-organisation, emergence and the creation of new order are three of the key
characteristics of complex systems. Kauffman in the ‘Origins of Order: Self-
Organization and Selection’ (1993) focuses on self-organisation and describes his
argument in the title. He calls Darwinian natural selection a “single singular force” and
argues that “It is this single-force view which I believe to be inadequate, for it fails to
notice, fails to stress, fails to incorporate the possibility that simple and complex systems
exhibit order spontaneously.” (Kauffman, 1993: xiii) That spontaneous order is self-
organisation; he brings all three characteristics together when he refers to “the
spontaneous emergence of order, the occurrence of self-organisation”. He argues that
natural selection is not the sole source of order in organisms and suggests that both
natural selection and self-organisation are necessary for evolution; he then proceeds to
expand evolutionary theory to incorporate both evolutionary forces.
Emergent properties, qualities, patterns, or structures, arise from the interaction of
individual elements; they are greater than the sum of the parts and may be difficult to
predict by studying the individual elements. Emergence is the process that creates new
order together with self-organisation.
In systems theory, emergence is related to the concept of the ‘whole’—i.e. that a
system may need to be studied as a complete and interacting whole rather than as an
assembly of distinct and separate elements. Checkland defines emergent properties as
those exhibited by a human activity system “as a whole entity, which derives from its
component activities and their structure, but cannot be reduced to them.” (Checkland
1981, p314) The emphasis is on the interacting whole and the non-reduction of those
properties to individual parts.
Francisco Varela (Varela & Maturana 1992, Varela 1995) in his study of the human
brain sees emergence as the transition from local rules or principles of interaction
20
between individual components or agents, to global principles or states encompassing
the entire collection of agents. Varela sees the transition from local to global rules of
interaction occurring as a result of explicit principles such as coherence and resonance,
which provide the local and global levels of analysis, (Varela 1995) but adds that to
understand emergence fully, we also need to understand the process that enables a
transition. The emergence of mental states for example, such as pattern recognition,
feelings and thoughts may be explained by the evolution of (macroscopic) (Varela’s
global principles or states) “order parameters of cerebral assemblies which are caused by
non-linear (microscopic)” (Varela’s local rules or principles) “interactions of neural
cells in learning strategies far from thermal equilibrium.” (Mainzer, 1996 p7). Another
area where the transition process is still not fully understood is that of human
consciousness. There is an ongoing debate between neuroscientists and philosophers as
to whether consciousness can be described as an emergent property of the neural activity
of the brain.
The relationship between the micro-events and macro-structures is not always in one
direction and there is reciprocal influence when feedback is in operation “One of the
most important problems in evolutionary theory is the eventual feedback between
macroscopic structures and microscopic events: macroscopic structures emerging from
microscopic events would in turn lead to a modification of the microscopic
mechanisms” (Prigogine & Stengers 1989). This is a co-evolutionary process whereby
the individual entities and the macro-structures they create through their interaction,
influence each other in an ongoing iterative process.
Modern thermodynamics describes the emergence of order by the mathematical
concepts of statistical mechanics (Mainzer, 1996 p4). Two kinds of phase transition
(self-organisation) for order states are distinguished: conservative and dissipative.
Conservative self-organisation means the phase transition of reversible structures in
thermal equilibrium, such as the growth of snow crystals, which can revert to water or
steam if the temperature is increased. Dissipative self-organisation is the phase transition
of irreversible structures far from thermal equilibrium. Macroscopic patterns emerge
from the complex non-linear cooperation of microscopic elements when the energetic
interaction of the dissipative (‘open’) system with its environment reaches some critical
value (Mainzer, 1996 p4). Nicolis (1994) adds “non-linear dynamics and the presence of
constraints maintaining the system far from equilibrium” are “the basic mechanisms
involved in the emergence of ... (self-organising) phenomena”.
In an organisational context, self-organisation may be described as the spontaneous
coming together of a group to perform a task (or for some other purpose); the group
decides what to do, how and when to do it; and no one outside the group directs those
activities. An example is what happened in an Integrated Project Team (IPT) in the
Aerospace industry. The team was brought together to create a new project. The
members of the team represented firms, which outside the IPT were competitors, but
within the team had to cooperate and to create an environment of trust to ensure that
sensitive information, necessary for the creation of the new product, could be freely
exchanged. The team had to prepare a six-monthly report for its various stakeholders.
21
This report was on hard copy and was usually several inches thick. Some members
within the team decided that they would try an alternative presentation. They found that
they had the requisite skills among them and they put in extra time to produce the next
report on a CD. The coming together of the sub-team to create the new format for the
report illustrates the principle of self-organisation. No one told them to do it or even
suggested it. They decided what to do, how and when to do it.
Emergence in a human system tends to create irreversible structures or ideas,
relationships and organisational forms, which become part of the history of individuals
and institutions and in turn affect the evolution of those entities: e.g. the generation of
knowledge and of innovative ideas when a team is working together could be described
as an emergent property in the sense that it arises from the interaction of individuals and
is not just the sum of existing ideas, but could well be something quite new and possibly
unexpected. Once the ideas are articulated they form part of the history of each
individual and part of the shared history of the team - the process is not reversible - and
these new ideas and new knowledge can be built upon to generate further new ideas and
knowledge. In the same way organisational learning is an emergent property - it is not
just reification (giving objective existence to a concept) but a process based on the
interaction of individuals creating new patterns of thought at the macro or organisational
level. When learning leads to new behaviours, then the organisation can be said to have
adapted and evolved. In that sense, learning is a prerequisite for organisational
evolution. If that is the case, then firms need to facilitate learning and the generation of
new knowledge - learning here does not mean just training or the acquisition of new
skills, but the gaining of insight and understanding which leads to new knowledge.
Continuing with this line of argument, the new knowledge needs to be shared, to
generate further new learning and knowledge. There are many reasons why this process
is severely limited in most organisations; one of those reasons may be that learning is
often seen exclusively as the provision of individual training and another is that the
generation and sharing of knowledge is identified with the capturing of data and
information in a database. This is not what the current argument is about. It is about
understanding connectivity, interdependence, emergence and self-organisation. It is
about how these characteristics of a human organisation, seen as a complex evolving
system, work together to create new order and coherence, to sustain the organisation and
to ensure its survival, particularly when its environment or social ecosystem is changing
fast.
Furthermore, the logic of complexity suggests that learning and the generation and
sharing of knowledge need to be facilitated by providing the appropriate socio-cultural
and technical conditions to support connectivity and interdependence and to facilitate
emergence and self-organisation. The latter two characteristics in particular are often
blocked or restricted even in what are considered to be liberal organisational cultures by
complicated authorisation procedures. It is not however the case that all emergent
properties and all self-organisation are necessarily desirable or efficacious. McKelvey
(Chapter 10, current volume) eloquently argues that under certain conditions emergence
could be “compromised, biased, fragile, sterile or maladaptive.” A negative side also
22
applies to connectivity. Again complexity theory does not argue for ever-increasing
connectivity, as there are limits to the viable connections that can be sustained and to the
information that any individual can handle, that arises from these connections.
To summarise, the main points are: (a) if we see organisations as complex evolving
systems and if we understand their characteristics as CES, we can work with those
characteristics rather than block them; (b) those characteristics are closely related and
we need to understand their interrelationship to gain maximum benefit from the
application of the theory; for example, looking at emergence or self-organisation in
isolation does not provide that deeper understanding; (c) to introduce the idea of
enabling environments based on socio-cultural and technical conditions that facilitate
rather than inhibit learning and the generation and sharing of knowledge; (d) to sound a
warning that connectivity cannot be increased indefinitely without breakdown and that
emergence is not always efficacious but can also become maladaptive.
7. Chaos and Complexity
Chaos Theory (Gleick 1987) is concerned with those forms of complexity in which
emergent order co-exists with disorder at the edge of chaos, a term coined by Chris
Langton (Waldrop 1992 - Penguin 1994: 230). When a system moves from a state of
order toward increasing disorder, it may go through a transition phase in which new
patterns of order emerge among the disorder, giving rise to the paradox of order co-
existing with disorder.
But Chaos Theory is not identical with complexity, and the two concepts need to be
distinguished in their application to social systems. Chaos theory describes non-linear
dynamics based on the iteration either of a mathematical algorithm or a set of simple
rules of interaction, both of which can give rise to extraordinarily intricate behaviour
such as the intricate beauty of fractals or the turbulence of a river. Brian Goodwin
(1997) describes such emergent patterns as the “emergent order (which) arises through
cycles of iteration in which a pattern of activity, defined by rules or regularities, is
repeated over and over again, giving rise to coherent order.” Therein lies the key
difference, because in chaos theory the iterated formula remains constant, while complex
systems may be capable of adapting and evolving, of changing their ‘rules’ of
interaction. Furthermore, “chaos by itself doesn’t explain the structure, the coherence,
the self-organizing cohesiveness of complex systems” (Waldrop 1992 - Penguin 1994:
12). Applying chaos theory to human systems therefore may not always be appropriate,
because human behaviour does not always mimic mathematical algorithms. Humans
have cognitive faculties that may enable them to change their rules of interaction.
7.1 Self-similarity
One of the features of complex systems is that similar characteristics may apply at
different levels and scales. In an organisational context, the generic characteristics of
complex systems may apply within a firm at different levels (individual, team,
corporate), as well as between related businesses and institutions, including direct and
23
indirect competitors, suppliers, and customers, as well as legal and economic systems.
Fractal is the term often used to describe the repetition of self-similar patterns across
levels or scale.
The concept of fractals is related to but distinct from the notion of ‘hierarchy’ in
systems theory. Hierarchy in the systems context does not refer to vertical relationships
of organisational structure or power, but rather to the notion of nested subsystems. It is
the interpretation of ‘subsystem’ that differs between the two theories. A fractal element
reflects and represents the characteristics of the whole, in the sense that similar patterns
of behaviour are found at different levels, while in systems theory, a subsystem is a part
of the whole, as well as being a whole in its own right. It is “equivalent to system, but
contained within a larger system” (Checkland 1981, p317). As Checkland (1981) notes,
hierarchy is “the principle according to which entities meaningfully treated as wholes
are built up of smaller entities which are themselves wholes … and so on. In a hierarchy,
emergent properties denote the levels” (Checkland 1981, p314). In fractals, repeated
properties denote the multiple levels of a system.
8. Managing Organisations as Complex Evolving Systems
If organisations were managed as complex evolving systems, co-evolving within a
social ecosystem, emergence would be facilitated rather than inhibited, and self-
organisation would be encouraged, as would exploration of the space of possibilities
available to an organisation. Managers would understand that an organisation is an entity
capable of creating new order, capable of re-creating itself. Management would focus on
the creation of conditions that facilitate constant co-evolution within a changing
environment, and would encourage the co-creation of new organisational form with
those directly affected.
We next consider one case study that describes efforts to implement such complexity
theory-based management approaches.
The Bank Case Study5
The European operation of an international bank needed to upgrade all its European
information systems to handle the common European currency by a rigid deadline that
could not be changed. The project was completed successfully and on time. One of the
main drivers was the pressure of legal and regulatory requirements that needed to be met
before the bank was ready to convert to the common European currency. However,
although the exogenous pressure was a necessary condition, it was not sufficient for
5 The Bank case study was written by Mitleton-Kelly and Papaefthimiou for the international workshop
on Feedback and Evolution in Software and Business Processes (FEAST) London, July 10-12 2000,
(Mitleton-Kelly and Papaefthimiou 2000, 2001)
24
success. Many other conditions needed to be created internally to provide a socio-
technical enabling infrastructure.
The project introduced new technologies, and because of its high profile imported an
international team of technical experts. What facilitated technical success were certain
social conditions initiated by the Project Manager in charge of the project. One of the
most important aspects was creating a closer working relationship between business and
information systems professionals than had been the norm in that particular organisation.
Previously, the system developers, business managers, and operations personnel simply
did not talk to each other unless absolutely necessary.
One of the project managers initiated a series of monthly meetings at which all three
constituencies had to be present and had to discuss their part of the project in a language
that was accessible to the others. The monthly meetings, supported by weekly
information updates, enabled the three managers of technology, business, and operations
to talk together regularly. Initially the meetings were not welcomed, but in time, the
various stakeholders involved in the projects began to identify cross-dependencies in the
business project relationships, which led to new insights and ideas for new ways of
working. Once conditions for new forms of communication were provided, the
individuals involved were able to self-organise, to make necessary decisions and take
appropriate actions. Communication enabled micro-agent interaction that was neither
managed nor controlled from the top. Once inhibitors were removed and enablers put in
place, new behaviours and ways of working emerged, making the business fitter and
more competitive.
Research identified some of the conditions that enabled the new way of working and
relating, as well as some of the conditions that could have restrained it.
Some of the enabling conditions were:
a) New procedures introducing regular monthly meetings, which supported
networking and the building of trust, as well as a common language leading to
mutual understanding.
b) Autonomy: the project manager was empowered to introduce new procedures.
c) A senior manager supported the changes, but did not interfere with the process.
d) Stability: sufficient continuity was assured to see the project through, in an
environment where constant change of personnel was a given.
e) An interpreter mediated the dialogue between the domains of expertise
represented at the meetings. This ensured understanding on both sides, but also
helped to protect the technologists from constant minor changes in requirements.
The potential inhibitors were:
25
a) Charging for system changes
b) Management discontinuity, resulting in projects not completed
c) Differing perceptions—e.g. improving legacy infrastructure could have been
seen as a cost by business managers, without understanding its compensating
benefits.
d) Loss of system expertise during the project, through restructuring, downsizing,
outsourcing, etc.
e) Lack of adequate documentation
f) Inaction when systems were seen as ‘old but reliable’
Another important element in this project was the articulation of business
requirements as an iterative process through regular face-to-face meetings. The business
requirements meetings in the Bank were at a senior management level with (a) a vice
president who owned the product, was responsible for the P&L (profit & loss) and
determined the business requirements, (b) a senior and experienced business project
manager who was a seasoned banker, with a good knowledge of the bank, and (c) a
senior technology project manager who defined the IS platform(s) and the technical
development of the project. This constant dialogue created a willingness to communicate
and a growing level of trust, both of which were essential enablers of co-evolution.
These social processes can also be seen as positive feedback or reinforcing processes.
For example, trust facilitates better communication, which in turn enables the building
of IT systems that facilitate both better communication and the evolution of the business.
What was achieved in this case involved a project manager, supported by his senior
manager, who created conditions that enabled dialogue, understanding, and a good
articulation of requirements. He created the initial conditions that improved the
relationships between the domains, but he could not exactly foresee how the process
would work, or indeed whether it would work. As it happened, it did work, and
substantial network rapport was established between the domains based on trust, a
common language, and mutual understanding. They worked well together because the
contextual conditions were right and they were prepared to self organise and work in a
different way. The new relationships that emerged were not designed beforehand. They
happened ‘spontaneously’ in the sense that they were enabled, but not stipulated.
The achievement in this case, however, could be a one-off event. Unless the new
procedures and ways of working used in this project become embedded in the culture of
the organisation, they may be forgotten over time. Once the project initiator moves on to
another position or organisation, dissipation or reversion to the dominant mode of
working may assert itself. In this case there has been some embedding to achieve
continuity, but the process is fragile. Much of the embedding is the networking rapport
26
that has been established, but the network rapport is implicit and informal, and is
therefore under threat if there are too many and too frequent changes. The Bank’s
culture is one of constant change in management positions, and if the rate and degree of
change is too great, then the networking and its ability to support emergent adaptations
may be lost.
Summary and Conclusions
This chapter introduces some of the principles of complexity based on the generic
characteristics of all complex systems. It uses the logic of complexity to argue for a
different approach to managing organisations through the identification, development,
and implementation of an enabling infrastructure, which includes the cultural, social,
and technical conditions that facilitate the day-to-day running of an organisation or the
creation of a new organisational form.
Enabling conditions are suggested using the principles of complexity. Complex
systems are not ‘designed’ in great detail. They are made up of interacting agents, whose
interactions create emergent properties, qualities, and patterns of behaviour. It is the
actions of individual agents and the immense variety of those actions that constantly
influence and create emergent macro patterns or structures. In turn the macro structure
of a complex ecosystem influences individual entities, and the evolutionary process
moves constantly between micro behaviours and emergent structures, each influencing
and recreating each other.
The complexity approach to managing is one of fostering, of creating enabling
conditions, of recognising that excessive control and intervention can be
counterproductive. When enabling conditions permit an organisation to explore its space
of possibilities, the organisation can take risks and try new ideas. Risk taking is meant to
help find new solutions, alternative ways to do business, to keep evolving through
established connectivities while establishing new ways of connecting (Mitleton-Kelly
2000).
This approach implies that all involved take responsibility for the decisions and
actions they carry out on behalf of the organisation. They should not take unnecessary
risks, nor are they blamed if the exploration of possibilities does not work. It is in the
nature of exploration that some solutions will work and some will not.
Thus, another aspect of an enabling infrastructure is the provision of space, both in
the metaphorical and actual senses. A good leader provides psychological space for
others to learn, but also physical space and resources for that learning to take place.
Individual and group learning is a prerequisite for adaptation, and the conditions for
learning and for the sharing of knowledge need to be provided.
Complexity’s great strength is that it crosses the boundaries of disciplines in both the
natural and social sciences. It may one day provide us with a unified approach capable
of linking those disciplines, because understanding the behaviour of complex systems in
27
other subjects helps one gain deeper insights into phenomena in one’s own field. Much
work now being done on complexity in a variety of fields, from anthropology and
psychology to economics and organisational science, will in due course change the way
we see organisations, will help us understand their nature as complex systems, and
ultimately will change the way that we manage organisations.
Eve Mitleton-Kelly
London School of Economics
June 2002
Acknowledgements
The chapter is based on research enabled through the support of the LSE Complexity
Research Programme collaborators and by two EPSRC (Engineering and Physical Science
Research Council) awards under the SEBPC Programme: IT & Computer Science Programme.
The first one-year preliminary study (GR/42834) of the two-phase project and the second 3-year
project (GR/MO2590) have been completed. The title for both projects was “The Implications of
the Theories of Complexity for the Co-evolution of the Business Process and Information
Systems Development”. Both projects explore the findings from the sciences of complexity and
examine the implications of generic characteristics of complex systems for organisations.
The EPSRC has also funded a third project, under the Innovative Manufacturing
Initiative, on improving the concept definition process (GR/M 23175). This was a
collaborative project with Warwick University, Cranfield Ecotechnology Centre and the
Aerospace Industry (Rolls-Royce, BAe Systems, GEC, Hunting Engineering, Smiths
Industries, DERA, IMI Marston, Pilatus Britten, Lucas Aerospace).
A fourth major award, also by the EPSRC is funding a 3-year collaborative project under the
Systems Integration Initiative entitled 'Enabling the Integration of Diverse Socio-cultural and
Technical Systems within a Turbulent Social Ecosystem' (GR/R37753). The industrial
collaborators are British Telecommunications, Norwich Union Life, Rolls Royce Marine and
Shell Internet Works. The project started on 1 September 2001.
The Complexity Programme industrial collaborators are both funding and research
partners. They have included BT, Citibank (New York), GlaxoSmithKline, the
Humberside TEC, Legal & General, Mondragon Cooperative Corporation (Basque
Country), Norwich Union, Rolls-Royce Marine, Shell (International, Finance and Shell
Internet Works), the World Bank (Washington DC), AstraZeneca and several companies
in the Aerospace industry including BAe Systems, DERA, GEC/Marconi, HS Marston,
Hunting Engineering, Lucas, Rolls-Royce and Smith Industries.
The research has also been enhanced by the Strategy & Complexity Seminar series, the
Study Groups on Complexity and Organisational Learning, the Complexity ‘Game’ and the
Complexity Consortium at the London School of Economics.
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